For the 3D body model, we use the prevalent Skinned Multi-Person Linear (SMPL) model, which is a statistical human body model that allows a compact representation using shape and pose parameters. The parameters consist of pose parameters \(\theta \in \mathbb {R}^{72}\) , shape parameters \(\beta \in \mathbb {R}^{10}\)  and weak-perspective camera parameters \(K\in \mathbb {R}^3\) . The model outputs a 3D mesh using a learned differentiable function \(M(\theta , \beta )\)  trained on 3D scans of people, and produces vertices \(V\in R^{N_v\times 3}, N_v=6890\) . In order to find the SMPL fittings, we use the SPIN model [1]} which exploits the good sides of both a deep network and an iterative optimization approach to estimate the 3D human pose and shape. SPIN uses a deep network to initialize an iterative optimization algorithm and in turn uses its output to supervise the network.
